Update README.md

README.md

@@ -10,38 +10,43 @@ metrics:
 pipeline_tag: image-classification
 ---
 
-Image Classification
+Fatima 2023 Application
+
 
 This project is about an image classification task of artificial and natural classes.
 
+
 Setup:
 
-  pip install -r requirements.txt
+    pip install -r requirements.txt
 
 
 Inference:
 
 
-  from torchvision import transforms
-  import torch
-  inference_transform = transforms.Compose([
-      transforms.Resize(128),
-      transforms.ToTensor(),
-      transforms.Normalize(mean=[0.4914, 0.4822, 0.4465],
-                           std=[0.2023, 0.1994, 0.2010]),
-  ])
-  
-  #load image and model
-  img_example = Image.open("image_example.png").convert('RGB')
-  print("image loaded!")
-  model_loaded = torch.load("fatima_challenge_model_exp3.pt")
-  model_loaded.eval()
-  print("model loaded!")
-  
-  
-  img_example_transformed = inference_transform(img_example)
-  out = model_loaded(img_example_transformed.to(torch.device("cuda:0")).unsqueeze(0)) # Generate predictions
-  _, outs = torch.max(out, 1)
-  prediction = "natural" if int(outs.cpu().numpy())==0 else "artificial"
-  print("prediction = ",prediction)
+    from torchvision import transforms
+    from PIL import Image
+    import torch
+    
+    
+    inference_transform = transforms.Compose([
+        transforms.Resize(128),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.4914, 0.4822, 0.4465],
+                             std=[0.2023, 0.1994, 0.2010]),
+    ])
+    
+    #load image and model
+    img_example = Image.open("image_example.png").convert('RGB')
+    print("image loaded!")
+    model_loaded = torch.load("fatima_challenge_model_exp3.pt")
+    model_loaded.eval()
+    print("model loaded!")
+    
+    
+    img_example_transformed = inference_transform(img_example)
+    out = model_loaded(img_example_transformed.to(torch.device("cuda:0")).unsqueeze(0)) # Generate predictions
+    _, outs = torch.max(out, 1)
+    prediction = "natural" if int(outs.cpu().numpy())==0 else "artificial"
+    print("prediction = ",prediction)